+++ /dev/null
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Contains common code shared by all inception models.
-
-Usage of arg scope:
- with slim.arg_scope(inception_arg_scope()):
- logits, end_points = inception.inception_v3(images, num_classes,
- is_training=is_training)
-
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-slim = tf.contrib.slim
-
-
-def inception_arg_scope(weight_decay=0.00004,
- use_batch_norm=True,
- batch_norm_decay=0.9997,
- batch_norm_epsilon=0.001):
- """Defines the default arg scope for inception models.
-
- Args:
- weight_decay: The weight decay to use for regularizing the model.
- use_batch_norm: "If `True`, batch_norm is applied after each convolution.
- batch_norm_decay: Decay for batch norm moving average.
- batch_norm_epsilon: Small float added to variance to avoid dividing by zero
- in batch norm.
-
- Returns:
- An `arg_scope` to use for the inception models.
- """
- batch_norm_params = {
- # Decay for the moving averages.
- 'decay': batch_norm_decay,
- # epsilon to prevent 0s in variance.
- 'epsilon': batch_norm_epsilon,
- # collection containing update_ops.
- 'updates_collections': tf.GraphKeys.UPDATE_OPS,
- }
- if use_batch_norm:
- normalizer_fn = slim.batch_norm
- normalizer_params = batch_norm_params
- else:
- normalizer_fn = None
- normalizer_params = {}
- # Set weight_decay for weights in Conv and FC layers.
- with slim.arg_scope([slim.conv2d, slim.fully_connected],
- weights_regularizer=slim.l2_regularizer(weight_decay)):
- with slim.arg_scope(
- [slim.conv2d],
- weights_initializer=slim.variance_scaling_initializer(),
- activation_fn=tf.nn.relu,
- normalizer_fn=normalizer_fn,
- normalizer_params=normalizer_params) as sc:
- return sc